First, we compare the process of statistics with the process of IML as defined in sec:DefIML on a formal level. The distinguishing between population, sample dataset and model is an inherently statistical view we already adopted in our formalization. IML in the classical sense interprets the model as given and often sets the sample dataset equal to the population, ignoring its representativeness. While IML applies queries to explain a model's prediction, statistics applies models to explain the data. Both processes evaluate or test their explanations, albeit through different procedures. The same applies to the human interpretation: While statistics has defined procedures on how to interpret and present the results (also in terms of scientific writing and reproducibility) [1]}, IML is just at the beginning and procedures differ from author to author.
